Anomaly Detection in Crowds Using Multi Sensory Information

Irfan, Muhammad; Tokarchuk, Laurissa; Regazzoni, Carlo S.

doi:10.1109/avss.2018.8639151

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Over the years, significant research efforts have been dedicated to developing sophisticated solutions and strategies for effective crowd management. Crowd management strategies include Crowd counting [12], [13], to quantify the number of individuals in a specific area; Density estimation [14]- [16], to estimate crowd density in a given area; Localization and tracking [17]- [20], to locate and track individuals or groups within a crowd of people; Behavior monitoring [21]- [24], to analyze and interpret individuals' actions within a crowd; and Anomaly detection [25]- [35], to identify unusual or unexpected events within the crowd.…”

Section: Related Workmentioning

confidence: 99%

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Crowd Management Intelligence Framework: Umrah Use Case

Halboob,

Altaheri,

Derhab

et al. 2024

IEEE Access

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Crowd management is crucial for countries and organizations as it can lead to severe consequences or serious safety concerns. Most of the existing research focus on addressing limited crowd management issues, namely crowd counting, density estimation, localization, and behavior monitoring. Furthermore, the generated incidents' alerts are mostly not interpretable and remediable. Therefore, there is no comprehensive solution that addresses all these issues. This research proposes a comprehensive intelligence-based crowd management framework that employs anomaly rules to monitor, predict, and detect crowd accidents and help in providing quick response. The suggested crowd intelligence framework addresses all crowd management issues. The use case chosen for this framework is the management of crowds of pilgrims in Umrah Holy event. The proposed framework is then implemented and evaluated with respect to efficiency, scalability, interpretability, remediability, and the number of false positive, true positive, and false negative alerts. In addition, the suggested framework is compared with other recent related work in terms of supporting crowd management issues. The design of the proposed framework and implementation are then fine-tuned in light of the evaluation results. The results and findings of this research can be extended to manage crowds at any event.

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Section: Related Workmentioning

confidence: 99%

“…Crowd management strategies mostly rely on camera-based data sources [12], [14], [16], [17], [21], [36] or sensor-based data sources [25], [37], while some innovative approaches combine camera and sensor data to improve crowd management outcomes [19], [22]. Studies in crowd management adopt two methodologies: simulation and modeling.…”

Section: Related Workmentioning

confidence: 99%

Crowd Management Intelligence Framework: Umrah Use Case

Halboob,

Altaheri,

Derhab

et al. 2024

IEEE Access

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“…The proposed algorithm was able to detect wrong-way driving on a T-junction. Anomalies were detected in crowds by Irfan et al [11]. The researchers classified the movement patterns into normal and abnormal activities using the Random Forest algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

Durr-e-Nayab¹,

Qamar²,

Khan³

et al. 2022

Computers, Materials &Amp; Continua

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The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks. Video surveillance and crowd management using video analysis techniques have significantly impacted today's research, and numerous applications have been developed in this domain. This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis. Managing the Kaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic. The Umrah videos are analyzed, and a system is devised that can track and monitor the crowd flow in Kaaba. The crowd in these videos is sparse due to the pandemic, and we have developed a technique to track the maximum crowd flow and detect any object (person) moving in the direction unlikely of the major flow. We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow. Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity to maintain a smooth crowd flow in Kaaba during the pandemic.

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“…Sensors such as accelerometer, gyroscope, and magnetometer, embedded in personal smart devices generate data that can be used to monitor users' activities, interactions, and mood [1,2,3]. Applications (apps) installed on smart devices can get access to raw sensor data to make required (i.e.…”

Section: Introductionmentioning

confidence: 99%

Privacy and utility preserving sensor-data transformations

Malekzadeh

Clegg

Cavallaro

et al. 2020

Pervasive and Mobile Computing

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Sensitive inferences and user re-identification are major threats to privacy when raw sensor data from wearable or portable devices are shared with cloud-assisted applications. To mitigate these threats, we propose mechanisms to transform sensor data before sharing them with applications running on users' devices. These transformations aim at eliminating patterns that can be used for user reidentification or for inferring potentially sensitive activities, while introducing a minor utility loss for the target application (or task). We show that, on gesture and activity recognition tasks, we can prevent inference of potentially sensitive activities while keeping the reduction in recognition accuracy of non-sensitive activities to less than 5 percentage points. We also show that we can reduce the accuracy of user re-identification and of the potential inference of gender to the level of a random guess, while keeping the accuracy of activity recognition comparable to that obtained on the original data.

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Anomaly Detection in Crowds Using Multi Sensory Information

Cited by 18 publications

References 17 publications

Crowd Management Intelligence Framework: Umrah Use Case

Crowd Management Intelligence Framework: Umrah Use Case

Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

Privacy and utility preserving sensor-data transformations

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